Fig. 1

The proposed architecture. The input point cloud is organized by mapping \(\Pi :\mathbb {R}^{n \times 3} \mapsto \mathbb {R}^{s_h \times s_w \times 3}\). Then, the ground segmentation, coupled with a clustering algorithm, generates simple proposals fed into the classifier neural network. Then, the first ID pass-through module discards coarse OOD proposals, which enables low computational requirements for the box estimation network. Similarly, the second ID pass-through module discards boxes that are OOD. In parallel, the PVLE encodes the locations of the proposals and feeds them into the classifier and the box estimator. The final output of the pipeline is 3D bounding boxes and class probabilities for the objects of interest